World Population Density |
Data: EC JRC & CIESIN Design: D A Smith CASA, UCL |
Residents per km2, 2020
| 20-99 | 400-1k | 2k-3.5k | 5.5k-7.5k | 10k-12k | 16k-22k | 30k-50k | 100k-200k | ||||||||
| 100-399 | 1k-2k | 3.5k-5.5k | 7.5k-10k | 12k-16k | 22k-30k | 50k-100k | 200k+ | ||||||||
World Population Density |
Data: EC JRC & CIESIN Design: D A Smith CASA, UCL |
| 20-99 | 400-1k | 2k-3.5k | 5.5k-7.5k | 10k-12k | 16k-22k | 30k-50k | 100k-200k | ||||||||
| 100-399 | 1k-2k | 3.5k-5.5k | 7.5k-10k | 12k-16k | 22k-30k | 50k-100k | 200k+ | ||||||||
This interactive map shows population density in 2020, measured in residents per square kilometre. The data is from the Global Human Settlement Layer (GHSL) 2023 produced by the European Commission JRC and the Center for International Earth Science Information Network at Columbia University. Integrating huge volumes of satellite data with national census data, the GHSL describes in detail the settlement geography of the entire globe, and has applications for a wide range of research and policy related to urban growth, development and sustainability. The GHSL records the complexity and diversity of human settlement, beyond simple rural-urban divisions.
This website has received 500,000 visitors since 2020, illustrating the widespread interest in global population geography. Some introductory highlights from the data are discussed below with links for further information.
At the global scale, the world population density map highlights the immense concentration of humanity in India and China. Both countries have a population of 1.4 billion, with India set to move ahead of China and reach 1.5 billion by 2030. While India has many of the world's largest cities, it retains a huge rural population of around 900 million people.
If we zoom in on India (click on links to focus the map), we can see the complexity of rural, peri-urban and urban landscapes, with thousands and thousands of villages, towns and cities in an intricate hierarchy. This is particularly the case along the Ganges plain in northern India, stretching nearly 2000km from just east of Delhi to Dhaka in Bangladesh. This is the world's largest agricultural region, supporting a population of around 450 million people in India and 120 million in Bangladesh.
In the early 20th century, geographers observed how rail and road networks were allowing rapidly growing cities to fuse together into vast sprawling conurbations. A classic example is the northeastern seaboard of the USA, a megalopolis as Gottman termed it, stretching hundreds of miles from Washington through New York City to Boston, with around 50m people.
In the last thirty years, China has undergone the largest process of urbanisation in history. This process has created several of the world's biggest megacity regions, mainly located on the coast, as the economic growth of these cities has been based on manufacturing for exports, business services, and increasingly high-technology industries. In the Pearl River Delta, Guangzhou, Shenzhen and Donguan have fused together to create a megacity region of 50m people (60m if Hong Kong and Macau are included). This 'Greater Bay Area' has grown from population of only 10m in 1980. A second megacity region is in the Yangtze River Delta, based around Shangai. Shanghai alone has a population of 28m, from a base of less than 7m in 1980.
Another related form of megaregion comes from areas of dense agriculture that begin to urbanise with looser patterns of small scale industry. McGee first used the term desakota ("village-city") in relation to the incredible form of Java in Indonesia, with the densities of urban hinterlands greatly exceeding Western cities but with activity patterns remaining dispersed and linked to agriculture. Similar desakota patterns can be seen in Kolkata, Dhaka, Lahore, and increasingly in several regions of Sub-Saharan Africa including Nigeria and surrounding Lake Victoria, though with much diversity in each case.
Urban densities are linked to cultures of living, with regions such as Latin America, South Asia and East Asia noted for high density urban forms. Higher population densities are also more prevalent in the Global South, as countries with poorer transport infrastructure need to use housing more intensively. The highest density cities in the world are in South Asia and Africa, such as Mumbai, Dhaka, Cairo and Kinshasa (note this depends how density is measured- see the Analysis page).
But these cities are more prosperous than neighbouring rural areas, and high densities can also be linked to affluence. Singapore, Hong Kong and Seoul combine extremely high densities with very high levels of prosperity. The richest large western cities, such as London, Paris, New York and San Francisco are the highest density cities in their respective countries.
Another important aspect of density is its relationship with travel demand and energy use. There are many examples of huge urban regions at very low densities, most evidently in the USA with metro regions such as Atlanta and Houston. Unsurprisingly these cities have the highest rates of transport energy use and carbon emissions in the world.
Sprawl is not however limited to the USA, and similar forms at a smaller scale can be seen in countries such as Australia and Canada. The industrial heartland of Europe that follows the river Rhine through western Germany to Belgium and the Netherlands, is notably dispersed and low density.
As well as exploring contemporary urbanism, many historic patterns remain engraved in the landscape of human settlement. Ancient cities first appeared five thousand years ago by fertile rivers that could support the intensive agricultre needed to feed an urban population. Egypt spectacularly displays the contrast between the arid Sahara and the rich lands fertilised by the Nile's annual inundation.
The oldest cities we know of were on the Tigris and Euphrates river deltas, near modern day Basra in Iraq. Another important ancient civilisation grew around the Indus Valley near Hyderabad in modern day Pakistan. The geography of population density in Pakistan is still closely linked to the Indus river today.
As well as rivers, some ancient transport links are visible in modern settlement patterns. The Roman road Via Aemilia cut across Northern Italy, through what is now Bologna and Parma. Its precise straight form is still evident 2000 years after its completion.
Analysis Page- Interactive statistics on country and city density profiles, and change over time.
Global Human Settlement Layer Data- The dataset used to make this visualisation, which is open and free to download.
Citygeographics- more information on this visualisation
The "Interactive Stats" checkbox at the top left of the map turns on density statistics for countries and cities which have been calculated from the GHSL data (1km scale). Roll your mouse over areas to see density and population statistics over time. Zoom in and out to switch between country and city statistics. The population data is from the United Nations World Population Prospects 2022, and includes a predicted population for 2030. More discussion on the density statistics can be found on this Citygeographics article.
Different countries around the world have radically different settlement patterns. If we look at the USA, which is renowned for lower density suburban living, the bar chart of population by density class peaks at 1-2km2.
The Population Weighted Density statistic summarises this chart, measuring 2.2k pp/km2 (persons per square km) for 2020. This is the lowest recorded density of any large developed country. Small countries such as Norway and New Zealand have similar Population Weighted Density results to the USA.
Latin America has for geographical and cultural reasons much higher population densities than the USA. Mexico has more than twice the Population Weighted Density at 5.7k pp/km2, while Colombia's is one of the highest in the world at 13.5k pp/km2, as the Colombian population is strongly concentrated in high density cities, such as Bogotá and Medellín.
The GHSL population data describes residents per square kilometre, based on underlying census data. This measure describes where people live, so areas such as Central Business Districts can appear low density if they do not include residents (see for example the very centre of London or Tokyo which are dominated by office activity and have few residents).
The Person Weighted Density represents the typical density that the population experiences in the region of interest (see article for more info). The Urban Land Area statistic uses the GHSL Settlement Model, and combines the urban, suburban and peri-urban categories at 1km scale.
If you zoom in on the map, then city level statistics can be viewed. The city boundaries are defined using urban centres from the GHSL Settlement Model data. This city definition is based on land use (built-up area and population density) rather than political/administrative boundaries, and emphasises continuous urban regions. The world's largest city regions using this definition are shown below-
| City Region name | Population 2020 |
| Guangzhou-Shenzhen-Donguan | 43.8m |
| Jakarta | 38.7m |
| Tokyo | 34.1m |
| Delhi | 30.3m |
| Shanghai | 27.8m |
| Dhaka | 26.8m |
| Kolkata | 26.7m |
| Manila | 24.8m |
| Cairo | 24.5m |
| Mumbai | 22.9m |
For more analysis of the city data, see the Citygeographics article.
The density classification and data scale used in the map influences its appearance. The classification used is non-linear (see map key top left), and the lower density classes represent changes of hundreds of people per square km, while the top classes represent changes of tens of thousands.
The scale of the underlying data is also important. The GHSL population data is available at 1km and 100m scales, and both layers are used here at different zoom levels. Finally the map is influenced by the map projection used, which in this case the Mercator projection, which shrinks the area of countries near the equator, and exaggerates the land area of countries in high latitudes such as Russia, Canada and Greenland. Note the statistics are all calculated using the original data projection (World Mollweide).